Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms

Tarun Jain, Tarun Jain and Vivek Kumar Verma, Vivek Kumar Verma and Akhilesh Kumar Sharma, Akhilesh Kumar Sharma and Bhavna Saini, Bhavna Saini and Nishant Purohit, Nishant Purohit and Bhavika, Bhavika and Mahdin, Hairulnizam and Ahmad, Masitah and Darman, Rozanawati and Su-Cheng Haw, Su-Cheng Haw and Shaharudin, Shazlyn Milleana and Arshad, Mohammad Syafwan (2023) Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms. International Journal of Advanced Computer Science and Applications, 14 (5). pp. 32-41.

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Abstract

One of the main functions of NLP (Natural Language Processing) is to analyze a sentiment or opinion of the text considered. In this research the objective is to analyze the sentiment in the form of tweets towards the Covid-19 vaccination. In this study, the collected tweets are in the form of a dataset from Kaggle that have been categorized into positive and negative depending on the polarity of the sentiment in that tweet, to visualize the overall situation. The reviews are translated into vector representations using various techniques, including BagOf-Words and TF-IDF to ensure the best result. Machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine (SVM) and others, and Deep Learning algorithms like LSTM and Bert were used to train the predictive models. The performance metrics used to test the performance of the models show that Support Vector Machine (SVM) achieved the highest accuracy of 88.7989% among the machine learning models. Compared to the related research papers the highest accuracy obtained using LSTM is 90.59 % and our model has predicted with the highest accuracy of 90.42% using BERT techniques.

Item Type:	Article
Uncontrolled Keywords:	Covid-19 vaccine; sentiment analysis; machine learning; deep learning; natural language processing
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Computer Science and Information Technology > FSKTM
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	25 Sep 2024 07:23
Last Modified:	25 Sep 2024 07:23
URI:	http://eprints.uthm.edu.my/id/eprint/11624

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